{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-jeecgboot--jeecgboot","slug":"jeecgboot--jeecgboot","name":"JeecgBoot","type":"product","url":"https://jeecg.com","page_url":"https://unfragile.ai/jeecgboot--jeecgboot","categories":["app-builders"],"tags":["activiti","agent","ai","antd","claude-code","cli","codegenerator","codex","flowable","langchain4j","llm","low-code","mcp","mybatis-plus","rag","skills","spring-ai","springboot","springcloud","vue3"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-jeecgboot--jeecgboot__cap_0","uri":"capability://code.generation.editing.ai.assisted.zero.code.system.generation.from.natural.language","name":"ai-assisted zero-code system generation from natural language","description":"Converts single-sentence natural language descriptions into complete working systems by leveraging LLM integration (via Spring-AI and LangChain4j) to interpret intent, generate data models, and orchestrate the OnlineCoding visual configuration engine. The system uses prompt engineering to extract entity definitions, relationships, and business rules from unstructured text, then maps these to the @jeecg/online form designer and database schema generator, producing executable applications without manual coding.","intents":["I want to describe a business process in plain English and have a working CRUD application generated immediately","I need to prototype a data management system without writing any code","I want to generate database schemas and UI forms from a single sentence description"],"best_for":["non-technical business analysts building internal tools","rapid prototyping teams with tight deadlines","enterprises reducing development time for standard CRUD applications"],"limitations":["LLM-based generation may produce suboptimal schema designs for complex relational models with many-to-many relationships","Natural language ambiguity can result in incorrect entity interpretation requiring manual refinement","Generated systems are limited to standard CRUD patterns; complex business logic requires code extension","Requires pre-configured LLM provider (OpenAI, Deepseek, etc.) with API credentials and rate limits"],"requires":["Spring Boot 3.5.5+","Vue 3 frontend environment","LLM API key (OpenAI, Deepseek, or compatible provider)","Database connection (MySQL, PostgreSQL, Oracle, or other supported DB)"],"input_types":["natural language text (single sentence or paragraph)","optional: existing database schema for augmentation"],"output_types":["Vue3 form components","Spring Boot REST API endpoints","SQL DDL statements for table creation","MyBatis-Plus entity classes"],"categories":["code-generation-editing","text-generation-language","low-code-platform"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.model.management.and.routing","name":"multi-provider llm model management and routing","description":"Provides a unified abstraction layer (via Spring-AI and jeecg-boot-module-airag) for managing multiple LLM providers (OpenAI, Deepseek, Anthropic, local Ollama instances) with dynamic model selection, fallback routing, and provider-agnostic prompt execution. The system maintains a model registry in the database, supports hot-swapping between providers without code changes, and includes cost tracking and usage analytics per model.","intents":["I want to switch between different LLM providers (OpenAI to Deepseek) without redeploying code","I need to route requests to the cheapest available model based on task complexity","I want to track which models are being used and their associated costs"],"best_for":["enterprises managing multiple LLM subscriptions and optimizing costs","teams building LLM-powered applications requiring provider flexibility","organizations with data residency requirements needing local model fallbacks"],"limitations":["Provider-specific features (vision, function calling, streaming) require adapter implementation for each new provider","Latency variance across providers (OpenAI ~500ms vs local Ollama ~2-5s) not automatically optimized","Model registry requires manual configuration; no automatic provider discovery","Cost tracking is approximate and depends on accurate token counting per provider"],"requires":["Spring Boot 3.5.5+","Spring-AI 1.0.0+","LangChain4j 0.20.0+","API keys for at least one LLM provider","Database for model registry storage"],"input_types":["prompt text","model identifier (string)","provider configuration (JSON)"],"output_types":["LLM response text","token usage metrics","provider routing decision logs"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_10","uri":"capability://safety.moderation.role.based.access.control.with.row.level.data.permissions","name":"role-based access control with row-level data permissions","description":"Implements a fine-grained authorization system combining role-based access control (RBAC) for feature/API access with row-level security (RLS) for data filtering. The system stores roles, permissions, and data permission rules in the database, evaluates permissions at the API layer using Spring Security interceptors, and applies row-level filters at the SQL query level using MyBatis-Plus interceptors. Data permissions can be based on user attributes (department, region) or custom business rules.","intents":["I want to restrict users to see only data from their department or region","I need to define which roles can access which APIs and features","I want to enforce data access policies consistently across all queries"],"best_for":["enterprises with complex organizational hierarchies requiring data isolation","multi-tenant applications enforcing tenant-level data separation","regulated industries (finance, healthcare) requiring audit-trail data access control"],"limitations":["Row-level permission evaluation adds SQL query overhead (~10-20% latency increase); no query optimization for permission filters","Complex permission rules (hierarchical, time-based, context-dependent) require custom filter implementation","Permission caching is manual; permission changes require cache invalidation","Cross-table permission enforcement is limited; permissions are evaluated per table","No built-in permission conflict resolution; overlapping rules require manual precedence definition"],"requires":["Spring Boot 3.5.5+","Spring Security 6.0+","MyBatis-Plus 3.5.0+","Database for role/permission storage"],"input_types":["user identity","requested resource (API endpoint or data table)","user attributes (department, region, etc.)"],"output_types":["permission decision (allow/deny)","filtered query results (with row-level filters applied)","audit log entry"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_11","uri":"capability://automation.workflow.microservices.architecture.with.nacos.service.discovery.and.spring.cloud.integration","name":"microservices architecture with nacos service discovery and spring cloud integration","description":"Supports microservices deployment using Spring Cloud Alibaba 2023.0.3.3 with Nacos for service discovery, configuration management, and load balancing. The system provides API Gateway routing, circuit breaker patterns via Sentinel, distributed tracing via Skywalking, and inter-service communication via Feign clients. Services can be deployed independently and registered with Nacos for dynamic discovery.","intents":["I want to deploy JeecgBoot as multiple independent microservices","I need automatic service discovery and load balancing across service instances","I want to manage configuration centrally and push updates without redeploying services"],"best_for":["large enterprises with complex service architectures","teams requiring independent service scaling and deployment","organizations needing centralized configuration management"],"limitations":["Microservices introduce distributed system complexity (eventual consistency, network latency, partial failures)","Nacos configuration management requires careful versioning; configuration changes can break dependent services","Inter-service communication latency (~50-200ms per hop) adds overhead vs monolithic calls","Distributed tracing (Skywalking) adds instrumentation overhead (~5-10% latency)","Service mesh (Istio) integration is not built-in; requires separate deployment and configuration"],"requires":["Spring Boot 3.5.5+","Spring Cloud Alibaba 2023.0.3.3+","Nacos 2.2.0+ (service discovery and config)","Sentinel 1.8.0+ (circuit breaker)","Skywalking 8.0+ (optional, for tracing)","Docker/Kubernetes for container orchestration"],"input_types":["service configuration (YAML)","Nacos service registry entries","inter-service API calls"],"output_types":["service instance list","routed API responses","distributed trace logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_12","uri":"capability://automation.workflow.distributed.transaction.management.with.seata.for.eventual.consistency","name":"distributed transaction management with seata for eventual consistency","description":"Implements distributed transaction support using Seata (Alibaba's distributed transaction framework) with AT (Automatic Transaction) mode for transparent transaction coordination across multiple databases. The system maintains transaction logs, supports rollback on failure, and ensures eventual consistency across services. Seata integrates with Spring Transaction management for seamless distributed transaction handling.","intents":["I want to ensure data consistency across multiple database operations in a microservices environment","I need automatic rollback if any step in a multi-service transaction fails","I want to avoid manual compensation logic for distributed transactions"],"best_for":["microservices applications requiring strong consistency guarantees","financial systems with strict transaction requirements","organizations migrating from monolithic to microservices architecture"],"limitations":["AT mode requires undo logs; adds ~20-30% overhead to transaction execution","Seata server is a single point of failure; requires clustering for high availability","Long-running transactions block resources; not suitable for workflows spanning hours/days","Deadlock risk increases with distributed transactions; requires careful transaction ordering","Rollback is eventual; intermediate states may be visible to other transactions"],"requires":["Spring Boot 3.5.5+","Seata 1.7.0+","Seata server deployment (separate process)","Database undo log tables (auto-created by Seata)"],"input_types":["transactional method (Java)","transaction configuration (annotations)"],"output_types":["transaction commit/rollback result","transaction log entries"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_13","uri":"capability://automation.workflow.electron.desktop.application.wrapper.for.offline.capable.pwa.deployment","name":"electron desktop application wrapper for offline-capable pwa deployment","description":"Packages the Vue3 frontend as an Electron desktop application with offline capabilities via PWA (Progressive Web App) service workers. The system caches critical assets and API responses, syncs data when connectivity is restored, and provides native desktop features (file system access, system tray integration). The Electron wrapper communicates with the Spring Boot backend via HTTP/WebSocket, supporting both online and offline modes.","intents":["I want to deploy JeecgBoot as a desktop application without browser dependency","I need offline functionality for field workers or unreliable network environments","I want to provide a native desktop experience with file system integration"],"best_for":["enterprises deploying applications to field workers with intermittent connectivity","organizations requiring desktop application distribution without app store approval","teams building hybrid web/desktop applications"],"limitations":["Electron adds ~150MB application size; distribution and updates are slower than web apps","Offline data sync is manual; complex conflict resolution requires custom implementation","Service worker caching strategy is basic; stale data issues require careful cache invalidation","Desktop features (file system, system tray) require Electron API knowledge; not all web features are available","Security model differs from web; requires careful handling of local data storage and IPC"],"requires":["Electron 25.0+","Vue 3 frontend","Service worker implementation (PWA)","Spring Boot backend for data sync"],"input_types":["Vue3 application bundle","PWA service worker configuration","Electron main process code"],"output_types":["Electron application executable (.exe, .dmg, .AppImage)","offline-cached data","sync queue for pending changes"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_14","uri":"capability://code.generation.editing.openapi.swagger.documentation.generation.with.automatic.api.discovery","name":"openapi/swagger documentation generation with automatic api discovery","description":"Automatically generates OpenAPI 3.0 specifications from Spring Boot controller annotations using Springdoc-OpenAPI, exposing interactive Swagger UI for API exploration and testing. The system introspects REST endpoints, request/response schemas, and validation rules, generating comprehensive API documentation without manual specification writing. Documentation is updated automatically when code changes.","intents":["I want to generate API documentation automatically from my Spring Boot code","I need an interactive API explorer for testing endpoints and understanding request/response formats","I want to keep API documentation in sync with code changes automatically"],"best_for":["development teams building REST APIs","API-first organizations requiring comprehensive documentation","teams integrating with external systems via APIs"],"limitations":["Complex request/response transformations (custom serializers) may not be reflected in generated schemas","Authentication/authorization details require manual annotation; not automatically inferred","Nested object schemas can become verbose; no automatic schema simplification","API versioning requires manual endpoint grouping; no automatic version detection","Generated documentation is read-only; customization requires manual editing"],"requires":["Spring Boot 3.5.5+","Springdoc-OpenAPI 2.0+","Spring Web annotations (@RestController, @RequestMapping, etc.)"],"input_types":["Spring Boot controller classes","request/response DTOs","validation annotations"],"output_types":["OpenAPI 3.0 specification (JSON/YAML)","Swagger UI (interactive HTML)","ReDoc documentation (alternative UI)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_2","uri":"capability://memory.knowledge.rag.based.knowledge.base.with.document.processing.and.semantic.search","name":"rag-based knowledge base with document processing and semantic search","description":"Implements a complete Retrieval-Augmented Generation pipeline (jeecg-boot-module-airag) that ingests documents (PDF, Word, text), chunks them using configurable strategies, generates embeddings via LLM providers, stores vectors in a vector database, and retrieves relevant context for LLM queries using semantic similarity search. The system uses LangChain4j for orchestration, supports multiple embedding models, and includes document metadata indexing for hybrid search (semantic + keyword filtering).","intents":["I want to upload company documentation and have an AI assistant answer questions based on that knowledge","I need to build a chatbot that retrieves relevant context from a large document corpus before generating responses","I want to index PDFs and Word documents for semantic search without manual annotation"],"best_for":["enterprises building internal knowledge base chatbots","customer support teams automating FAQ responses with document grounding","organizations with large document repositories needing intelligent search"],"limitations":["Chunking strategy (fixed size, sliding window, semantic) significantly impacts retrieval quality; no automatic optimization","Embedding quality depends on chosen model; domain-specific embeddings may require fine-tuning","Vector database selection (Milvus, Weaviate, Pinecone) affects scalability and latency; no built-in multi-vector-DB abstraction","Hallucination risk remains if retrieved context is insufficient or contradictory; requires prompt engineering to mitigate","Document processing pipeline lacks OCR for scanned PDFs; text extraction limited to digital PDFs"],"requires":["Spring Boot 3.5.5+","LangChain4j 0.20.0+","Vector database (Milvus, Weaviate, Pinecone, or compatible)","Document storage (local filesystem or cloud storage)","Embedding model API access (OpenAI, Hugging Face, or local)"],"input_types":["PDF files","Word documents (.docx)","plain text files","user query (natural language)"],"output_types":["retrieved document chunks (with relevance scores)","augmented LLM response (with source citations)","search result rankings"],"categories":["memory-knowledge","data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_3","uri":"capability://planning.reasoning.ai.workflow.orchestration.with.visual.flow.designer.and.dynamic.node.execution","name":"ai workflow orchestration with visual flow designer and dynamic node execution","description":"Provides a visual workflow designer (AIFlow module) that allows users to compose AI-driven processes by connecting nodes (LLM calls, data transformations, conditional logic, API calls) in a DAG structure. The system executes workflows using an event-driven engine that processes nodes asynchronously, maintains execution state, supports branching/looping, and integrates with the knowledge base and model management systems. Workflows are stored as JSON configurations and can be triggered via REST API or scheduled execution.","intents":["I want to design a multi-step AI process (extract data → enrich with LLM → validate → store) without coding","I need to create conditional workflows that route based on LLM output or data conditions","I want to schedule recurring AI tasks (daily report generation, batch processing) visually"],"best_for":["business analysts designing AI-powered business processes","teams automating multi-step data enrichment pipelines","organizations building event-driven AI applications"],"limitations":["Complex conditional logic (nested if-else, loops) becomes difficult to visualize at scale; workflows with >20 nodes become hard to manage","Error handling and retry logic require manual node configuration; no automatic exponential backoff","Workflow execution is single-threaded per instance; parallel node execution not supported","Debugging failed workflows requires log inspection; no built-in workflow replay or step-through debugging","State persistence requires external storage; no built-in workflow state snapshots for recovery"],"requires":["Spring Boot 3.5.5+","Vue 3 frontend for visual designer","Database for workflow definition storage","Message queue (optional, for async execution)","LLM provider API keys for LLM nodes"],"input_types":["workflow JSON definition","trigger data (REST payload, scheduled event)","node configuration (LLM prompts, API endpoints, data mappings)"],"output_types":["workflow execution result","execution logs and metrics","transformed/enriched data"],"categories":["planning-reasoning","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_4","uri":"capability://text.generation.language.conversational.ai.chat.service.with.multi.turn.context.management.and.skill.integration","name":"conversational ai chat service with multi-turn context management and skill integration","description":"Implements a chat service (jeecg-boot-module-airag) that maintains conversation history, manages token-aware context windows, integrates with the knowledge base for RAG-augmented responses, and supports skill/tool calling for external API integration. The system uses LangChain4j for conversation memory management, supports multiple conversation threads per user, and includes message persistence for audit and analytics.","intents":["I want to build a chatbot that remembers previous messages in a conversation","I need the chat to call external APIs or tools based on user intent","I want to augment chat responses with knowledge base context automatically"],"best_for":["customer support teams building AI-powered chat interfaces","enterprises implementing internal knowledge assistants","teams building multi-turn conversational agents"],"limitations":["Context window management is manual; no automatic summarization of old messages when approaching token limits","Skill/tool calling requires explicit function definition; no automatic function discovery from APIs","Conversation state is in-memory by default; distributed deployments require external session store configuration","No built-in conversation branching (exploring alternative responses); only linear conversation history","Token counting is approximate; actual usage may exceed estimates, causing mid-conversation truncation"],"requires":["Spring Boot 3.5.5+","LangChain4j 0.20.0+","Database for message persistence","LLM provider API keys","Optional: external session store (Redis) for distributed deployments"],"input_types":["user message (text)","conversation ID","optional: skill/tool definitions (JSON)"],"output_types":["assistant response (text)","tool call requests (JSON)","conversation metadata (turn count, tokens used)"],"categories":["text-generation-language","memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_5","uri":"capability://code.generation.editing.code.generation.from.database.schema.and.visual.form.definitions","name":"code generation from database schema and visual form definitions","description":"Generates complete full-stack code (Vue3 frontend + Spring Boot backend + MyBatis-Plus entities) from database schemas and form definitions using the CodeGenerateUtil class and OnlineCoding configuration. The generator introspects database tables, infers CRUD operations, generates typed entity classes, REST endpoints with pagination/filtering, and Vue3 form components with validation rules. Generated code is immediately executable and can be customized via code templates.","intents":["I want to generate a complete CRUD application from an existing database schema","I need to create REST APIs and form UIs automatically from table definitions","I want to generate boilerplate code that I can then customize"],"best_for":["developers accelerating CRUD application development","teams with existing database schemas needing rapid API generation","enterprises standardizing on code generation patterns"],"limitations":["Generated code follows opinionated patterns (MyBatis-Plus, Spring Data JPA); customization requires template modification","Complex business logic (computed fields, cross-table aggregations) requires manual code addition","Relationship handling (one-to-many, many-to-many) generates basic foreign key references; complex joins require manual implementation","Generated validation rules are basic (required, length, type); complex cross-field validation requires code extension","Template customization requires understanding FreeMarker or Velocity syntax; no visual template editor"],"requires":["Spring Boot 3.5.5+","Maven 3.6+","Database connection with read access to schema metadata","Vue 3 development environment","MyBatis-Plus 3.5.0+"],"input_types":["database table definitions","form field configuration (JSON)","optional: code generation templates"],"output_types":["Java entity classes (with JPA annotations)","Spring Boot REST controllers","Vue3 form components","MyBatis-Plus mapper interfaces","SQL DDL statements"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_6","uri":"capability://code.generation.editing.visual.form.designer.with.dynamic.field.validation.and.conditional.rendering","name":"visual form designer with dynamic field validation and conditional rendering","description":"Provides a drag-and-drop form designer (@jeecg/online package) that allows users to compose forms by selecting field types, configuring validation rules, and defining conditional visibility/enable logic without coding. The designer generates form JSON configurations that are rendered at runtime by Vue3 components, supporting complex field types (date pickers, selects with dynamic options, file uploads, rich text editors) and cross-field validation rules.","intents":["I want to design a data entry form without writing HTML or JavaScript","I need to create forms with conditional fields that show/hide based on other field values","I want to add validation rules (required, email format, numeric range) visually"],"best_for":["business users designing data collection forms","rapid prototyping teams building data-driven applications","organizations standardizing form patterns across applications"],"limitations":["Complex custom field types require component development; designer supports only built-in field types","Conditional logic is limited to simple if-then rules; complex nested conditions require JSON editing","Form submission handling is basic (POST to API); complex multi-step workflows require code extension","Styling customization is limited to theme variables; custom CSS requires code modification","No built-in form versioning; schema changes may break existing form instances"],"requires":["Vue 3 development environment","Spring Boot 3.5.5+ backend for form definition storage","Database for form configuration persistence"],"input_types":["field type selection (text, number, date, select, etc.)","validation rule configuration (JSON)","conditional logic rules (JSON)"],"output_types":["form JSON configuration","Vue3 form component","form submission payload (JSON)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_7","uri":"capability://automation.workflow.bpm.workflow.designer.with.activiti.flowable.integration.for.business.process.automation","name":"bpm workflow designer with activiti/flowable integration for business process automation","description":"Integrates with Activiti and Flowable workflow engines to provide a visual BPMN 2.0 process designer that allows users to model business processes (tasks, gateways, events, subprocesses) and execute them with human task assignment, process variables, and audit trails. The system stores process definitions in the database, manages task queues, supports process versioning, and integrates with the user/role system for task assignment and approval workflows.","intents":["I want to design an approval workflow (request → manager approval → finance approval → execution) visually","I need to assign tasks to users based on roles and process variables","I want to track process execution history and audit who approved what and when"],"best_for":["enterprises automating business processes (approvals, onboarding, procurement)","organizations with complex multi-step workflows requiring audit trails","teams managing human-in-the-loop processes"],"limitations":["Complex process logic (dynamic task assignment, conditional subprocesses) requires process variable configuration; visual designer has limited expressiveness","Task assignment is role-based; dynamic assignment based on external data requires custom task listener implementation","Process versioning can cause issues with running instances; migration between versions requires manual handling","Performance degrades with large process instance volumes (>100k instances); no built-in archival strategy","Integration with external systems (HR systems, approval APIs) requires custom service task implementation"],"requires":["Spring Boot 3.5.5+","Activiti 7.0+ or Flowable 6.7+","Database for process definition and instance storage","User/role system for task assignment"],"input_types":["BPMN 2.0 process definition (XML or visual design)","process variables (JSON)","task assignment rules"],"output_types":["process instance ID","task list (for assigned users)","process execution history and audit trail","process variable state"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_8","uri":"capability://data.processing.analysis.multi.database.support.with.automatic.dialect.handling.and.data.sharding","name":"multi-database support with automatic dialect handling and data sharding","description":"Supports multiple database backends (MySQL, PostgreSQL, Oracle, SQL Server, H2) with automatic SQL dialect translation via MyBatis-Plus and database-specific configuration. The system uses ShardingSphere for transparent data sharding across multiple database instances, supporting both range-based and hash-based sharding strategies. Database migrations are managed via Flyway with dialect-specific migration scripts.","intents":["I want to switch databases (MySQL to PostgreSQL) without changing application code","I need to shard data across multiple database instances for scalability","I want to manage database schema migrations automatically"],"best_for":["enterprises with multi-database environments","high-scale applications requiring data sharding","teams managing database migrations across environments"],"limitations":["Sharding configuration is static; dynamic shard rebalancing not supported","Cross-shard queries (joins across sharded tables) require application-level aggregation; no automatic distributed join","Sharding key selection is manual; poor choice leads to data skew and hotspots","Flyway migrations are sequential; parallel migrations not supported","Database-specific features (window functions, JSON operators) require manual SQL tuning per dialect"],"requires":["Spring Boot 3.5.5+","MyBatis-Plus 3.5.0+","ShardingSphere 5.3.0+ (for sharding)","Flyway 9.0+ (for migrations)","Multiple database instances (for sharding)"],"input_types":["database connection configuration","sharding rules (key, strategy)","migration SQL scripts"],"output_types":["query results from selected database","sharded data distribution","migration execution logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeecgboot--jeecgboot__cap_9","uri":"capability://tool.use.integration.plugin.and.mcp.system.for.extending.platform.capabilities","name":"plugin and mcp system for extending platform capabilities","description":"Provides a plugin architecture (jeecg-boot-plugin module) that allows developers to extend platform functionality by implementing standard plugin interfaces and registering them via Spring component scanning. The system supports Model Context Protocol (MCP) for standardized tool/skill integration, enabling plugins to expose capabilities as callable functions with schema-based validation. Plugins can hook into lifecycle events (startup, shutdown, data changes) and access core services via dependency injection.","intents":["I want to add custom business logic (domain-specific calculations, external API integrations) as a reusable plugin","I need to expose my plugin's capabilities as callable tools for AI workflows and chat","I want to package and distribute my extensions without modifying core platform code"],"best_for":["platform developers extending JeecgBoot for specific use cases","teams building domain-specific plugins (industry verticals, custom integrations)","ISVs distributing JeecgBoot-based solutions with custom extensions"],"limitations":["Plugin isolation is limited; plugins share the same JVM and can interfere with each other","No built-in plugin versioning; multiple versions of the same plugin cannot coexist","Plugin dependency management is manual; no automatic dependency resolution","MCP schema validation is basic; complex nested schemas require custom validation logic","Plugin marketplace/distribution is not built-in; requires external package management"],"requires":["Spring Boot 3.5.5+","Java 17+","Plugin interface implementation (Java)","MCP schema definition (JSON)"],"input_types":["plugin class (Java)","MCP function schema (JSON)","plugin configuration (properties/YAML)"],"output_types":["plugin registration confirmation","callable function endpoints","plugin execution results"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Spring Boot 3.5.5+","Vue 3 frontend environment","LLM API key (OpenAI, Deepseek, or compatible provider)","Database connection (MySQL, PostgreSQL, Oracle, or other supported DB)","Spring-AI 1.0.0+","LangChain4j 0.20.0+","API keys for at least one LLM provider","Database for model registry storage","Spring Security 6.0+","MyBatis-Plus 3.5.0+"],"failure_modes":["LLM-based generation may produce suboptimal schema designs for complex relational models with many-to-many relationships","Natural language ambiguity can result in incorrect entity interpretation requiring manual refinement","Generated systems are limited to standard CRUD patterns; complex business logic requires code extension","Requires pre-configured LLM provider (OpenAI, Deepseek, etc.) with API credentials and rate limits","Provider-specific features (vision, function calling, streaming) require adapter implementation for each new provider","Latency variance across providers (OpenAI ~500ms vs local Ollama ~2-5s) not automatically optimized","Model registry requires manual configuration; no automatic provider discovery","Cost tracking is approximate and depends on accurate token counting per provider","Row-level permission evaluation adds SQL query overhead (~10-20% latency increase); no query optimization for permission filters","Complex permission rules (hierarchical, time-based, context-dependent) require custom filter implementation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.42587684995755665,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:01.479Z","last_commit":"2026-04-30T09:27:11Z"},"community":{"stars":46070,"forks":15960,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=jeecgboot--jeecgboot","compare_url":"https://unfragile.ai/compare?artifact=jeecgboot--jeecgboot"}},"signature":"r44D8cgQqOVtOuU9ysvUQPUmfshe8/nU7JcYg19cose5ijjJMZeFqHSy4MyhxRkP75y8UUPhwZMYsAUkiX/ZBA==","signedAt":"2026-06-19T21:51:28.986Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jeecgboot--jeecgboot","artifact":"https://unfragile.ai/jeecgboot--jeecgboot","verify":"https://unfragile.ai/api/v1/verify?slug=jeecgboot--jeecgboot","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}